File size: 5,150 Bytes
c34e33c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 |
{
"cells": [
{
"cell_type": "markdown",
"id": "d792f35b",
"metadata": {},
"source": [
"# Lesson 2: Interacting with a CSV Data"
]
},
{
"cell_type": "markdown",
"id": "6be6d049",
"metadata": {},
"source": [
"## Setup and connect to the Azure OpenAI endpoint"
]
},
{
"cell_type": "markdown",
"id": "399c89a2",
"metadata": {},
"source": [
"**Note**: The pre-configured cloud resource grants you access to the Azure OpenAI GPT model. The key and endpoint provided below are intended for teaching purposes only. Your notebook environment is already set up with the necessary keys, which may differ from those used by the instructor during the filming."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "83ac3e59-d0d1-4110-a059-3a55d0d5e15e",
"metadata": {
"height": 217
},
"outputs": [],
"source": [
"import os \n",
"import pandas as pd\n",
"\n",
"from IPython.display import Markdown, HTML, display\n",
"from langchain.schema import HumanMessage\n",
"from langchain_openai import AzureChatOpenAI\n",
"\n",
"model = AzureChatOpenAI(\n",
" openai_api_version=\"2023-05-15\",\n",
" azure_deployment=\"gpt-4-1106\",\n",
" azure_endpoint=os.getenv(\"AZURE_OPENAI_ENDPOINT\"),\n",
")"
]
},
{
"cell_type": "markdown",
"id": "ba9075a3",
"metadata": {},
"source": [
"## Load the dataset"
]
},
{
"cell_type": "markdown",
"id": "fb0dc855",
"metadata": {},
"source": [
"**Note**: To access the data locally, use the following code:\n",
"\n",
"```\n",
"os.makedirs(\"data\",exist_ok=True)\n",
"!wget https://covidtracking.com/data/download/all-states-history.csv -P ./data/\n",
"file_url = \"./data/all-states-history.csv\"\n",
"df = pd.read_csv(file_url).fillna(value = 0)\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b6c75799-55e2-4d39-a1ab-7b968b4f35a4",
"metadata": {
"height": 30
},
"outputs": [],
"source": [
"df = pd.read_csv(\"./data/all-states-history.csv\").fillna(value = 0)"
]
},
{
"cell_type": "markdown",
"id": "a6a261ac",
"metadata": {},
"source": [
"## Prepare the Langchain dataframe agent"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "60d0793b-90e7-4e8b-b2f1-66a8d1cf652d",
"metadata": {
"height": 115
},
"outputs": [],
"source": [
"from langchain.agents.agent_types import AgentType\n",
"from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent\n",
"\n",
"agent = create_pandas_dataframe_agent(llm=model,df=df,verbose=True)\n",
"\n",
"agent.invoke(\"how many rows are there?\")"
]
},
{
"cell_type": "markdown",
"id": "7c6dedce",
"metadata": {},
"source": [
"## Design your prompt and ask your question"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0d005008-bad6-4458-90ec-6372d9ebe61c",
"metadata": {
"height": 523
},
"outputs": [],
"source": [
"CSV_PROMPT_PREFIX = \"\"\"\n",
"First set the pandas display options to show all the columns,\n",
"get the column names, then answer the question.\n",
"\"\"\"\n",
"\n",
"CSV_PROMPT_SUFFIX = \"\"\"\n",
"- **ALWAYS** before giving the Final Answer, try another method.\n",
"Then reflect on the answers of the two methods you did and ask yourself\n",
"if it answers correctly the original question.\n",
"If you are not sure, try another method.\n",
"- If the methods tried do not give the same result,reflect and\n",
"try again until you have two methods that have the same result.\n",
"- If you still cannot arrive to a consistent result, say that\n",
"you are not sure of the answer.\n",
"- If you are sure of the correct answer, create a beautiful\n",
"and thorough response using Markdown.\n",
"- **DO NOT MAKE UP AN ANSWER OR USE PRIOR KNOWLEDGE,\n",
"ONLY USE THE RESULTS OF THE CALCULATIONS YOU HAVE DONE**.\n",
"- **ALWAYS**, as part of your \"Final Answer\", explain how you got\n",
"to the answer on a section that starts with: \"\\n\\nExplanation:\\n\".\n",
"In the explanation, mention the column names that you used to get\n",
"to the final answer.\n",
"\"\"\"\n",
"\n",
"QUESTION = \"How may patients were hospitalized during July 2020\" \n",
"\"in Texas, and nationwide as the total of all states?\"\n",
"\"Use the hospitalizedIncrease column\" \n",
"\n",
"\n",
"agent.invoke(CSV_PROMPT_PREFIX + QUESTION + CSV_PROMPT_SUFFIX)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|